13 citations · 13 across the 6 of their papers we have counts for
6 papers
Volumetric Material Decomposition Using Spectral Diffusion Posterior Sampling with a Compressed Polychromatic Forward Model
Xiao Jiang, Grace J. Gang, J. Webster Stayman
We have previously introduced Spectral Diffusion Posterior Sampling (Spectral DPS) as a framework for accurate one-step material decomposition by integrating analytic spectral syst…
CTorch: PyTorch-Compatible GPU-Accelerated Auto-Differentiable Projector Toolbox for Computed Tomography
Xiao Jiang, Grace J. Gang, J. Webster Stayman
This work introduces CTorch, a PyTorch-compatible, GPU-accelerated, and auto-differentiable projector toolbox designed to handle various CT geometries with configurable projector a…
Multi-Material Decomposition Using Spectral Diffusion Posterior Sampling
Xiao Jiang, Grace J. Gang, J. Webster Stayman
Many spectral CT applications require accurate material decomposition. Existing material decomposition algorithms are often susceptible to significant noise magnification or, in th…
Deep Learning CT Image Restoration using System Blur and Noise Models
Yijie Yuan, Grace J. Gang, J. Webster Stayman
The restoration of images affected by blur and noise has been widely studied and has broad potential for applications including in medical imaging modalities like computed tomograp…
Strategies for CT Reconstruction using Diffusion Posterior Sampling with a Nonlinear Model
Xiao Jiang, Shudong Li, Peiqing Teng +2
Diffusion Posterior Sampling(DPS) methodology is a novel framework that permits nonlinear CT reconstruction by integrating a diffusion prior and an analytic physical system model,…
CT Reconstruction using Diffusion Posterior Sampling conditioned on a Nonlinear Measurement Model
Shudong Li, Xiao Jiang, Matthew Tivnan +3
Diffusion models have been demonstrated as powerful deep learning tools for image generation in CT reconstruction and restoration. Recently, diffusion posterior sampling, where a s…